Actually …

People requested that I look at the relationship between the change in mean temperature and the change in the frequency of extreme heat, not using temperature at the 850 hPa level (about 1.5 km altitude), not using a reanalysis data set which doesn’t incorporate any actual temperature measurements, not using anomaly data, and not restricting analysis to Dec-Jan-Feb (winter months in the northern hemisphere).

Let’s try to do this right. Let’s use actual temperature data from actual thermometers. Let’s look at temperature at earth’s surface, where we actually live. Let’s use temperature rather than temperature anomaly, since that’s what actally defines what a region would experience as extreme heat. Since I’ll be looking at northern hemisphere locations, let’s use data from Jun-Jul-Aug, actual summer months.

I acquired daily temperature data from ECA&D (European Climate Assessment & Dataset). I then isolated the data from June, July, and August. In order for a year to be included, it had to have data for at least 85 of the 92 summer-month days. In order for a station to be included, it had to have data for at least 100 years since 1900, including at least one year in the 2010’s. That left 167 locations (mostly in Europe, entirely in the northern hemisphere) with sufficient data to be included.

For each year, I tallied the number of hot days. “Hot” is defined relative to what was normal for that location prior to 1980. The cutoff limit was chosen as the 97.5th percentile, which for a normal distribution corresponds to 1.96 standard deviations above the mean (but no normal-distribution assumptions are involved in the actual analysis).

I then compared mean summertime daily temperature for the first 30 years of data, to that for the last 30 years of data, and compared the number of hot days for the same time spans.

The result? Here’s a graph of the change in mean temperature between those two 30-year periods; red circles mark locations where the temperature increased, blue circles where it decreased, and the size of the circle indicates how much the temperature changed:

Here’s a graph of the change in the total number of hot days during the 30-year time span, red circles for locations where there were more hot days, blue circles for locations with fewer hot days, and the size of the cirlce indicating how much the number changed:

Comparison of these graphs reveals that the pattern of changes in the number of hot days is quite similar to the pattern of changes in mean temperature. Yes, the increase in extreme heat is strongly correlated with the increase in mean temperature.

This strongly and unambiguously contradicts the idea that “The pattern of the change in extreme warm daily temperature probabilities looks nothing like the mean warming pattern.” For the few locations I’ve examined, the same contradiction is found if, instead of doing it right, you mimic Sardeshmukh’s procedure and use reanalysis data at the 850 hPa level, using temperature anomaly rather than temperature, restricted to Dec-Jan-Feb, but use a different reanalysis data set (from ECMWF). The same contradiction is also found if you use Dec-Jan-Feb reanalysis data for temperature at the surface (T2m) rather than the 850 hPa level.

All of which argues that IF the results of Sardeshmukh et al. are correct for the data set they used, then there just might be quite a problem with that data set because it’s contradicted by other reanalysis data, surface temperature data from reanalysis, data from summer months for the northern hemisphere, and of course, actual thermometer data.

If the sole purpose of Sardeshmukh et al. is to demonstrate that changes in the probability of extremes depends on changes in the probability distribution’s shape as well as its mean and standard deviation, then their work is rather trivial. If, however, their purpose is to suggest that this phenomenon is presently happening on actual planet Earth, then they seriously need to reconsider.

Let’s use actual temperature data from actual thermometers. Let’s look at temperature at earth’s surface, where we actually live. Let’s use temperature rather than temperature anomaly, since that’s what actally defines what a region would experience as extreme heat. Since I’ll be looking at northern hemisphere locations, let’s use data from Jun-Jul-Aug, actual summer months.

And what a radical approach that is… any possibility of adding an XY plot with the change in # of hot days vs. change in mean temperature?

With respect to the number of hot days there’s a noticeable difference between western and central Europe compared to continental Asia, particularly in the former Soviet Union. Any possibility of a systematic bias in the Russian/USSR/Russian dataset?

I’ve been following this debate/discussion with interest. A couple of years ago, I did an analysis that may be somewhat relevant to this discussion. I looked at Minneapolis, MN (I grew up there, so was interested) daily temperature data from 1950-2012, looking separately at daily highs and daily lows. I averaged daily highs and lows by month, and looked to see if there were significant trends over the years for each month. From 1950-1978, there were no trends either in highs or lows for any month (even a slight, but not significant, decrease). From 1978 through 2012, upward trends became obvious. What was interesting was that the trends were stronger for daily lows than for daily highs, and, for June-August, there were no significant trends in the daily high temperatures, but there were for the daily lows. I’ve updated this with data through 2014, and looked at frequency distributions for daily highs and lows for 1978-1982 and, separately, 2010-2014 (i.e., the first and last 5 year period in this 36 year period of warming, a total of 460 data points for each 5 year period). Again, the most pronounced changes are in the frequency distribution of the daily lows, with not much change at the high end of the daily high temp distribution for the June-August period (the hottest period of the year in Minneapolis and presumably through most of the northern hemisphere). Since daily average temperatures will be some hybrid of daily lows and highs, it thus seems theoretically possible, that on planet earth, at least in one location and during a particular time period, the shape of the probability distribution of temperature may change in such a way that increases in the mean may not translate (at least not yet in this one place) into increases in the frequency of temperature extremes. Note that I am not arguing that rising temperatures are innocuous.
These trends seem consistent with my understanding of global warming theory in that changes in daily lows may be larger than changes in daily high temperatures. This is exactly the pattern I observed for this period in Minneapolis.
I note that I was unable to attach pictures of the frequency distribution curves. If you would like to see them, let me know.
Also, if I have somehow totally screwed up this analysis, I assume you’ll let me know that too.

[Response: A very interesting point. ECA&D provides data for daily maximum temperature also, so I’ll re-run the analysis using that.]

Thanks for all these posts. It is extremely frustrating that no amount of truth gets anywhere with the insistent drumbeat coming from the phony skeptic universe.

The fact that this is dangerous to an extreme (pun intended) seems to be irrelevant to them. It’s politics politics all the way, and forget reality, that doesn’t exist. And even if it does, god wouldn’t let it happen or somebody will come up with some magic solution because everybody’s getting rich (ugh).

I don’t have anything new to contribute except that it is sickening that so many fine intelligent people keep on saying the same thing, except that they keep mentioning that it’s getting worse (which it is), and the resistance just gets fiercer and more dishonest by the day.

Off topic, but this is one place that presents the information in stark reality (h/t for link to DotEarth, though the most recent there is more of what one might expect).

“Climate strongly influences global wildfire activity, and recent wildfire surges may signal fire weather-induced pyrogeographic shifts. Here we use three daily global climate data sets and three fire danger indices to develop a simple annual metric of fire weather season length, and map spatio-temporal trends from 1979 to 2013. We show that fire weather seasons have lengthened across 29.6 million km2 (25.3%) of the Earth’s vegetated surface, resulting in an 18.7% increase in global mean fire weather season length. We also show a doubling (108.1% increase) of global burnable area affected by long fire weather seasons (>1.0 σ above the historical mean) and an increased global frequency of long fire weather seasons across 62.4 million km2 (53.4%) during the second half of the study period.”

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